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R interface to fast.ai

Home Page: https://eagerai.github.io/fastai/

License: Apache License 2.0

R 18.16% Dockerfile 0.05% Python 1.38% HTML 80.41%

fastai r vision text collaborative-filtering tabular medical audio darknet darknet-image-classification

fastai's Introduction

R interface to fastai

The fastai package provides R wrappers to fastai.

The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for vision, text, tabular, and collab (collaborative filtering) models.

Continuous Build Status

Build	Status
Bionic
Focal
Mac OS
Windows

Installation

1. Install miniconda and activate environment:

reticulate::install_miniconda()
reticulate::conda_create('r-reticulate')

2. The dev version:

devtools::install_github('eagerai/fastai')

3. Later, you need to install the python module fastai:

reticulate::use_condaenv('r-reticulate',required = TRUE)
fastai::install_fastai(gpu = FALSE, cuda_version = '11.6', overwrite = FALSE)

4. Restart RStudio!

fast.ai extensions:

Kaggle

We currently prepare the examples of usage of the fastai from R in Kaggle competitions:

Contributions are very welcome!

Tabular data

library(magrittr)
library(fastai)

# download
URLs_ADULT_SAMPLE()

# read data
df = data.table::fread('adult_sample/adult.csv')

Variables:

dep_var = 'salary'
cat_names = c('workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race')
cont_names = c('age', 'fnlwgt', 'education-num')

Preprocess strategy:

procs = list(FillMissing(),Categorify(),Normalize())

Prepare:

dls = TabularDataTable(df, procs, cat_names, cont_names,
      y_names = dep_var, splits = list(c(1:32000),c(32001:32561))) %>%
      dataloaders(bs = 64)

Summary:

model = dls %>% tabular_learner(layers=c(200,100), metrics=accuracy)
model %>% summary()

TabularModel (Input shape: ['64 x 7', '64 x 3'])
================================================================
Layer (type)         Output Shape         Param #    Trainable
================================================================
Embedding            64 x 6               60         True
________________________________________________________________
Embedding            64 x 8               136        True
________________________________________________________________
Embedding            64 x 5               40         True
________________________________________________________________
Embedding            64 x 8               136        True
________________________________________________________________
Embedding            64 x 5               35         True
________________________________________________________________
Embedding            64 x 4               24         True
________________________________________________________________
Embedding            64 x 3               9          True
________________________________________________________________
Dropout              64 x 39              0          False
________________________________________________________________
BatchNorm1d          64 x 3               6          True
________________________________________________________________
BatchNorm1d          64 x 42              84         True
________________________________________________________________
Linear               64 x 200             8,400      True
________________________________________________________________
ReLU                 64 x 200             0          False
________________________________________________________________
BatchNorm1d          64 x 200             400        True
________________________________________________________________
Linear               64 x 100             20,000     True
________________________________________________________________
ReLU                 64 x 100             0          False
________________________________________________________________
Linear               64 x 2               202        True
________________________________________________________________

Total params: 29,532
Total trainable params: 29,532
Total non-trainable params: 0

Optimizer used: <function Adam at 0x7fa246283598>
Loss function: FlattenedLoss of CrossEntropyLoss()

Callbacks:
  - TrainEvalCallback
  - Recorder
  - ProgressCallback

Before fitting try to find optimal learning rate:

model %>% lr_find()

model %>% plot_lr_find(dpi = 200)

Run:

model %>% fit(5, lr = 10^-1)

epoch     train_loss  valid_loss  accuracy  time
0         0.360149    0.329587    0.846702  00:04
1         0.352106    0.345761    0.828877  00:04
2         0.368743    0.340913    0.844920  00:05
3         0.347277    0.333084    0.852050  00:04
4         0.348969    0.350707    0.830660  00:04

Plot loss history:

model %>% plot_loss(dpi = 200)

See training process:

Get confusion matrix:

model %>% get_confusion_matrix()

       <50k  >=50k
<50k   407    22
>=50k   68    64

Plot it:

interp = ClassificationInterpretation_from_learner(model)

interp %>% plot_confusion_matrix(dpi = 90,figsize = c(6,6))

Get predictions on new data:

> model %>% predict(df[10:15,])

       <50k     >=50k classes
1 0.5108562 0.4891439       0
2 0.4827824 0.5172176       1
3 0.4873166 0.5126833       1
4 0.5013804 0.4986197       0
5 0.4964157 0.5035844       1
6 0.5111378 0.4888622       0

Image data

Get Pets dataset:

URLs_PETS()

Define path to folders:

path = 'oxford-iiit-pet'
path_anno = 'oxford-iiit-pet/annotations'
path_img = 'oxford-iiit-pet/images'
fnames = get_image_files(path_img)

See one of examples:

fnames[1]

oxford-iiit-pet/images/american_pit_bull_terrier_129.jpg

Dataloader:

dls = ImageDataLoaders_from_name_re(
  path, fnames, pat='(.+)_\\d+.jpg$',
  item_tfms=Resize(size = 460), bs = 10,
  batch_tfms=list(Normalize_from_stats( imagenet_stats() )
                  )
)

Show batch for visualization:

dls %>% show_batch()

Model architecture:

learn = cnn_learner(dls, resnet34(), metrics = error_rate)

And fit:

learn %>% fit_one_cycle(n_epoch = 2)

epoch     train_loss  valid_loss  error_rate  time
0         0.904872    0.317927    0.105548    00:35
1         0.694395    0.239520    0.083897    00:36

Get confusion matrix and plot:

conf = learn %>% get_confusion_matrix()

library(highcharter)
hchart(conf, label = TRUE) %>%
    hc_yAxis(title = list(text = 'Actual')) %>%
    hc_xAxis(title = list(text = 'Predicted'),
             labels = list(rotation = -90))

Note that the plot is built with highcharter.

Plot top losses:

interp = ClassificationInterpretation_from_learner(learn)

interp %>% plot_top_losses(k = 9, figsize = c(15,11))

Alternatively, load images from folders:

# get sample data
URLs_MNIST_SAMPLE()

# transformations
path = 'mnist_sample'
bs = 20

#load into memory
data = ImageDataLoaders_from_folder(path, size = 26, bs = bs)

# Visualize and train
data %>% show_batch(dpi = 150)

learn = cnn_learner(data, resnet18(), metrics = accuracy)
learn %>% fit(2)

What about the implementation of the latest Computer Vision models?

There is a function in fastai timm_learner which originally written by Zachary Mueller. It helps to quickly load the pretrained models from timm library.

First, lets's see the list of available models (TOP 10):

> str(as.list(timm_list_models()[1:10]))
List of 10
 $ : chr "adv_inception_v3"
 $ : chr "cspdarknet53"
 $ : chr "cspdarknet53_iabn"
 $ : chr "cspresnet50"
 $ : chr "cspresnet50d"
 $ : chr "cspresnet50w"
 $ : chr "cspresnext50"
 $ : chr "cspresnext50_iabn"
 $ : chr "darknet53"
 $ : chr "densenet121"

Exciting!

Now, load and train pets dataset:

library(magrittr)
library(fastai)

path = 'oxford-iiit-pet'

path_img = 'oxford-iiit-pet/images'

fnames = get_image_files(path_img)

dls = ImageDataLoaders_from_name_re(
  path, fnames, pat='(.+)_\\d+.jpg$',
  item_tfms=Resize(size = 460), bs = 10,
  batch_tfms=list(Normalize_from_stats( imagenet_stats() )
  )
)

learn = timm_learner(dls, 'cspdarknet53', metrics = list(accuracy, error_rate))

learn %>% summary()

Model summary

Sequential (Input shape: ['10 x 3 x 224 x 224'])
================================================================
Layer (type)         Output Shape         Param #    Trainable
================================================================
Conv2d               10 x 32 x 224 x 224  864        False
________________________________________________________________
LeakyReLU            10 x 32 x 224 x 224  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  18,432     False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 128 x 112 x 11  8,192      False
________________________________________________________________
LeakyReLU            10 x 128 x 112 x 11  0          False
________________________________________________________________
Conv2d               10 x 32 x 112 x 112  2,048      False
________________________________________________________________
LeakyReLU            10 x 32 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  18,432     False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 64 x 112 x 112  8,192      False
________________________________________________________________
LeakyReLU            10 x 64 x 112 x 112  0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   73,728     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    36,864     False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    36,864     False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 64 x 56 x 56    4,096      False
________________________________________________________________
LeakyReLU            10 x 64 x 56 x 56    0          False
________________________________________________________________
Conv2d               10 x 128 x 56 x 56   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 56 x 56   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   294,912    False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   147,456    False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 128 x 28 x 28   16,384     False
________________________________________________________________
LeakyReLU            10 x 128 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 256 x 28 x 28   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 28 x 28   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   1,179,648  False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   589,824    False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 256 x 14 x 14   65,536     False
________________________________________________________________
LeakyReLU            10 x 256 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 512 x 14 x 14   262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 14 x 14   0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    4,718,592  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    1,048,576  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     2,359,296  False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 512 x 7 x 7     262,144    False
________________________________________________________________
LeakyReLU            10 x 512 x 7 x 7     0          False
________________________________________________________________
Conv2d               10 x 1024 x 7 x 7    1,048,576  False
________________________________________________________________
LeakyReLU            10 x 1024 x 7 x 7    0          False
________________________________________________________________
AdaptiveAvgPool2d    10 x 1024 x 1 x 1    0          False
________________________________________________________________
AdaptiveMaxPool2d    10 x 1024 x 1 x 1    0          False
________________________________________________________________
Flatten              10 x 2048            0          False
________________________________________________________________
BatchNorm1d          10 x 2048            4,096      True
________________________________________________________________
Dropout              10 x 2048            0          False
________________________________________________________________
Linear               10 x 512             1,048,576  True
________________________________________________________________
ReLU                 10 x 512             0          False
________________________________________________________________
BatchNorm1d          10 x 512             1,024      True
________________________________________________________________
Dropout              10 x 512             0          False
________________________________________________________________
Linear               10 x 37              18,944     True
________________________________________________________________

Total params: 27,654,496
Total trainable params: 1,072,640
Total non-trainable params: 26,581,856

Optimizer used: <function Adam at 0x7fc1cfc16f28>
Loss function: FlattenedLoss of CrossEntropyLoss()

Model frozen up to parameter group #1

Callbacks:
  - TrainEvalCallback
  - Recorder
  - ProgressCallback

And finally, fit:

learn %>% fit_one_cycle(3)

epoch   train_loss   valid_loss   accuracy   error_rate   time
------  -----------  -----------  ---------  -----------  ------
0       1.206384     0.518956     0.847091   0.152909     01:00
1       0.841627     0.411970     0.890392   0.109608     00:58
2       0.657220     0.328548     0.899188   0.100812     00:59

See results:

learn %>% show_results()

Impressive!

GAN example

Get data (4,4 GB):

URLs_LSUN_BEDROOMS()

path = 'bedroom'

Dataloader function:

get_dls <- function(bs, size) {
  dblock = DataBlock(blocks = list(TransformBlock(), ImageBlock()),
                     get_x = generate_noise(),
                     get_items = get_image_files(),
                     splitter = IndexSplitter(c()),
                     item_tfms = Resize(size, method = "crop"),
                     batch_tfms = Normalize_from_stats(c(0.5,0.5,0.5), c(0.5,0.5,0.5))
  )
  dblock %>% dataloaders(source = path, path = path,bs = bs)
}

dls = get_dls(128, 64)

Generator and discriminator:

generator = basic_generator(out_size = 64, n_channels = 3, n_extra_layers = 1)
critic    = basic_critic(in_size = 64, n_channels = 3, n_extra_layers = 1,
                                    act_cls = partial(nn$LeakyReLU, negative_slope = 0.2))

Model:

learn = GANLearner_wgan(dls, generator, critic, opt_func = partial(Adam(), mom=0.))

And fit:

learn$recorder$train_metrics = TRUE
learn$recorder$valid_metrics = FALSE

learn %>% fit(1, 2e-4, wd = 0)

epoch     train_loss  gen_loss  crit_loss  time
0         -0.555554   0.516327  -0.967604  05:06

This is the result for 1 epoch.

learn %>% show_results(max_n = 16, figsize = c(8,8), ds_idx=0)

Unet example

Call libraries:

library(fastai)
library(magrittr)

Get data

URLs_CAMVID()

Specify folders:

path = 'camvid'
fnames = get_image_files(paste(path,'images',sep = '/'))
lbl_names = get_image_files(paste(path,'labels',sep = '/'))
codes = data.table::fread(paste(path,'codes.txt',sep = '/'), header = FALSE)[['V1']]
valid_fnames = data.table::fread(paste(path,'valid.txt',sep = '/'),header = FALSE)[['V1']]
# batch size
bs = 8

Define a loader object:

camvid = DataBlock(blocks = c(ImageBlock(), MaskBlock(codes)),
                   get_items = get_image_files,
                   splitter = FileSplitter('camvid/valid.txt'),
                   get_y = function(x) {paste('camvid/labels/',x$stem,'_P',x$suffix,sep = '')},
                   batch_tfms = list(Normalize_from_stats( imagenet_stats() )
                   )
)

# prefix and suffix of the name of the file
x$stem; x$suffix

Dataloader object and list of labels:

dls = camvid %>% dataloaders(source = "camvid/images", bs = bs, path = path)

dls %>% show_batch()

void_code = which(codes == "Void")

dls$vocab = codes

name2id = as.list(1:(length(codes)))
names(name2id) = codes

str(name2id)
List of 32
 $ Animal           : int 1
 $ Archway          : int 2
 $ Bicyclist        : int 3
 $ Bridge           : int 4
 $ Building         : int 5
 $ Car              : int 6
 $ CartLuggagePram  : int 7
 $ Child            : int 8
 $ Column_Pole      : int 9
 $ Fence            : int 10
 $ LaneMkgsDriv     : int 11
 $ LaneMkgsNonDriv  : int 12
 $ Misc_Text        : int 13
 $ MotorcycleScooter: int 14
 $ OtherMoving      : int 15
 $ ParkingBlock     : int 16
 $ Pedestrian       : int 17
 $ Road             : int 18
 $ RoadShoulder     : int 19
 $ Sidewalk         : int 20
 $ SignSymbol       : int 21
 $ Sky              : int 22
 $ SUVPickupTruck   : int 23
 $ TrafficCone      : int 24
 $ TrafficLight     : int 25
 $ Train            : int 26
 $ Tree             : int 27
 $ Truck_Bus        : int 28
 $ Tunnel           : int 29
 $ VegetationMisc   : int 30
 $ Void             : int 31
 $ Wall             : int 32

Custom accuracy function:

acc_camvid <- function(input, target) {
  target = target$squeeze(1L)
  # exclude/filter void label
  mask = target != void_code
  return(
    (input$argmax(dim=1L)[mask]$eq(target[mask])) %>%
      float() %>% mean()
  )
}

attr(acc_camvid, "py_function_name") <- 'acc_camvid'

Debug acc_camvid manually

batch = dls %>% one_batch(convert = FALSE)

[[1]]
TensorImage([[[[-1.4419e+00, -1.3117e+00, -1.1976e+00,  ...,  2.2489e+00,
            2.2238e+00,  2.0948e+00],
          [-1.5401e+00, -1.5213e+00, -1.4010e+00,  ...,  1.9834e+00,
            2.2378e+00,  2.2173e+00],
          [-1.6401e+00, -1.5477e+00, -1.5588e+00,  ...,  9.1953e-01,
            1.9501e+00,  1.1138e+00],
          ...,
          [-1.6852e+00, -1.5440e+00, -1.5132e+00,  ..., -1.0596e+00,
           -1.0711e+00, -1.0674e+00],
          [-1.5265e+00, -1.6030e+00, -1.5804e+00,  ..., -1.0268e+00,
           -1.0946e+00, -1.1181e+00],
          [-1.5423e+00, -1.5516e+00, -1.6014e+00,  ..., -1.1734e+00,
           -1.1293e+00, -1.0777e+00]],

         [[-1.3446e+00, -1.2023e+00, -1.0470e+00,  ...,  2.4286e+00,
            2.4090e+00,  2.2977e+00],
          [-1.4481e+00, -1.4276e+00, -1.2930e+00,  ...,  2.1422e+00,
            2.4158e+00,  2.3778e+00],
          [-1.5607e+00, -1.4584e+00, -1.4641e+00,  ...,  1.0026e+00,
            2.0258e+00,  1.1376e+00],
          ...,
          [-1.5809e+00, -1.4399e+00, -1.4133e+00,  ..., -7.8931e-01,
           -7.9807e-01, -7.9637e-01],
          [-1.4161e+00, -1.4909e+00, -1.4646e+00,  ..., -8.0615e-01,
           -8.5201e-01, -8.5311e-01],
          [-1.4472e+00, -1.4567e+00, -1.5077e+00,  ..., -9.4607e-01,
           -8.9744e-01, -8.2074e-01]],

         [[-1.1164e+00, -1.0162e+00, -9.1189e-01,  ...,  2.6257e+00,
            2.5726e+00,  2.4016e+00],
          [-1.2195e+00, -1.1752e+00, -1.0595e+00,  ...,  2.3488e+00,
            2.6271e+00,  2.5764e+00],
          [-1.3316e+00, -1.2451e+00, -1.2400e+00,  ...,  1.0476e+00,
            2.1812e+00,  1.3635e+00],
          ...,
          [-1.2881e+00, -1.1393e+00, -1.1035e+00,  ..., -3.8940e-01,
           -4.0598e-01, -3.9861e-01],
          [-1.1427e+00, -1.2167e+00, -1.1906e+00,  ..., -3.6462e-01,
           -4.3055e-01, -4.5333e-01],
          [-1.1525e+00, -1.1651e+00, -1.2190e+00,  ..., -4.8259e-01,
           -4.3712e-01, -4.1413e-01]]],


        [[[-2.0552e-01,  3.9563e-01,  4.0691e-01,  ..., -9.7342e-01,
           -7.8957e-01, -7.6035e-01],
          [-3.8852e-01,  4.2912e-01,  4.4469e-01,  ..., -1.0449e+00,
           -8.5347e-01, -7.5299e-01],
          [ 3.5939e-01,  3.6353e-01,  4.7028e-01,  ..., -9.3101e-01,
           -8.7398e-01, -7.9327e-01],
          ...,
          [-1.0510e+00, -1.0661e+00, -9.6690e-01,  ..., -1.3688e+00,
           -1.4543e+00, -1.4645e+00],
          [-1.0578e+00, -1.0939e+00, -9.3117e-01,  ..., -1.3939e+00,
           -1.4033e+00, -1.4209e+00],
          [-9.9012e-01, -1.0312e+00, -1.0074e+00,  ..., -1.4274e+00,
           -1.3829e+00, -1.3758e+00]],

         [[ 6.0090e-02,  7.8124e-01,  7.5145e-01,  ..., -8.2881e-01,
           -6.7773e-01, -6.3718e-01],
          [-1.7114e-01,  7.8613e-01,  7.8531e-01,  ..., -9.0003e-01,
           -7.3661e-01, -5.8707e-01],
          [ 7.3440e-01,  7.5691e-01,  8.2297e-01,  ..., -8.0694e-01,
           -7.5451e-01, -6.2783e-01],
          ...,
          [-7.8971e-01, -7.8585e-01, -7.4870e-01,  ..., -1.2630e+00,
           -1.3108e+00, -1.3046e+00],
          [-7.8414e-01, -7.9617e-01, -7.2847e-01,  ..., -1.2297e+00,
           -1.2414e+00, -1.2594e+00],
          [-7.3135e-01, -7.7442e-01, -7.4849e-01,  ..., -1.2259e+00,
           -1.1889e+00, -1.2022e+00]],

         [[ 4.4920e-01,  1.2392e+00,  1.3399e+00,  ..., -6.0991e-01,
           -4.5250e-01, -4.4251e-01],
          [ 2.7577e-01,  1.2913e+00,  1.3755e+00,  ..., -6.8060e-01,
           -5.1114e-01, -3.7442e-01],
          [ 1.0632e+00,  1.3052e+00,  1.3774e+00,  ..., -5.8343e-01,
           -5.2787e-01, -3.9803e-01],
          ...,
          [-4.4165e-01, -4.4558e-01, -3.8942e-01,  ..., -8.7048e-01,
           -9.2835e-01, -9.2750e-01],
          [-4.4233e-01, -4.6348e-01, -3.7176e-01,  ..., -8.6960e-01,
           -8.8080e-01, -8.9788e-01],
          [-3.8967e-01, -4.3118e-01, -3.8587e-01,  ..., -8.7933e-01,
           -8.4775e-01, -8.5052e-01]]],


        [[[ 1.2805e+00,  2.2139e+00,  9.9765e-01,  ...,  6.6338e-01,
           -4.0192e-01,  2.8007e-01],
          [ 1.0171e+00,  1.8849e+00,  1.1654e+00,  ..., -1.0001e+00,
            1.1788e+00,  2.0717e+00],
          [ 2.8709e-01,  1.9494e+00,  2.1978e+00,  ..., -6.7389e-01,
            3.2762e-01,  4.5549e-01],
          ...,
          [-4.3609e-01, -4.2635e-01, -4.6298e-01,  ...,  7.7548e-02,
            3.6271e-02, -3.1759e-02],
          [-3.7265e-01, -4.3453e-01, -4.4666e-01,  ..., -7.5601e-02,
            5.3570e-03, -2.9393e-02],
          [-3.7581e-01, -4.0105e-01, -4.2908e-01,  ...,  8.5172e-03,
           -3.3988e-03, -1.8303e-02]],

         [[ 1.3276e+00,  2.3720e+00,  1.0603e+00,  ...,  8.6043e-01,
           -1.1662e-01,  5.2147e-01],
          [ 1.0938e+00,  2.0233e+00,  1.2629e+00,  ..., -9.1610e-01,
            1.3807e+00,  2.2914e+00],
          [ 3.8840e-01,  2.1078e+00,  2.3635e+00,  ..., -5.8584e-01,
            5.2653e-01,  7.8300e-01],
          ...,
          [-3.1636e-01, -3.0640e-01, -3.4385e-01,  ...,  1.3784e-01,
            9.5460e-02,  2.5607e-02],
          [-2.5150e-01, -3.1476e-01, -3.2716e-01,  ..., -1.9409e-02,
            6.3717e-02,  2.8037e-02],
          [-2.5473e-01, -2.8054e-01, -3.0920e-01,  ...,  6.6963e-02,
            5.4727e-02,  3.9424e-02]],

         [[ 1.8118e+00,  2.6126e+00,  1.5284e+00,  ...,  1.3408e+00,
            3.8263e-01,  9.4347e-01],
          [ 1.4345e+00,  2.2263e+00,  1.5055e+00,  ..., -4.0407e-01,
            1.9165e+00,  2.5325e+00],
          [ 6.9120e-01,  2.3214e+00,  2.5724e+00,  ..., -5.9273e-02,
            7.6707e-01,  9.8036e-01],
          ...,
          [-3.2707e-02, -2.5592e-02, -6.5520e-02,  ...,  3.1733e-01,
            2.8317e-01,  2.2166e-01],
          [ 1.6474e-02, -4.1773e-02, -5.1314e-02,  ...,  1.6267e-01,
            2.4836e-01,  2.1449e-01],
          [ 2.4832e-02,  1.0270e-02, -1.5259e-02,  ...,  2.3768e-01,
            2.2930e-01,  2.2220e-01]]],


        ...,


        [[[-1.5176e-02, -1.9729e-02, -5.4177e-02,  ...,  2.0812e+00,
            2.2489e+00,  2.2242e+00],
          [-1.0897e-02,  3.5695e-02,  2.3053e-03,  ...,  2.1605e+00,
            2.0372e+00,  2.1403e+00],
          [-2.8262e-02, -3.0313e-02, -3.4347e-02,  ...,  2.2136e+00,
            2.2489e+00,  1.2613e+00],
          ...,
          [-1.2644e+00, -1.2548e+00, -1.2313e+00,  ..., -1.3335e+00,
           -1.3230e+00, -1.2787e+00],
          [-1.1986e+00, -1.2068e+00, -1.1631e+00,  ..., -1.2694e+00,
           -1.2973e+00, -1.2696e+00],
          [-1.2508e+00, -1.2447e+00, -1.2294e+00,  ..., -1.0572e+00,
           -1.0660e+00, -1.0694e+00]],

         [[ 2.2227e-01,  2.1430e-01,  2.1605e-01,  ...,  2.3389e+00,
            2.4286e+00,  2.4286e+00],
          [ 2.0176e-01,  2.4693e-01,  2.4092e-01,  ...,  2.3745e+00,
            2.2931e+00,  2.3820e+00],
          [ 1.8103e-01,  1.7892e-01,  1.7477e-01,  ...,  2.4036e+00,
            2.4286e+00,  1.4878e+00],
          ...,
          [-1.0710e+00, -1.0613e+00, -1.0374e+00,  ..., -1.2492e+00,
           -1.2385e+00, -1.2225e+00],
          [-1.0040e+00, -1.0124e+00, -9.6780e-01,  ..., -1.1836e+00,
           -1.2122e+00, -1.2193e+00],
          [-1.0572e+00, -1.0510e+00, -1.0354e+00,  ..., -9.5631e-01,
           -9.6512e-01, -9.6444e-01]],

         [[ 5.4786e-01,  5.5583e-01,  5.3839e-01,  ...,  2.5781e+00,
            2.6400e+00,  2.6400e+00],
          [ 5.3558e-01,  5.8483e-01,  5.6649e-01,  ...,  2.5895e+00,
            2.5283e+00,  2.6400e+00],
          [ 5.2345e-01,  5.2294e-01,  5.1033e-01,  ...,  2.6400e+00,
            2.6400e+00,  1.7087e+00],
          ...,
          [-8.1354e-01, -8.0387e-01, -7.9721e-01,  ..., -1.0014e+00,
           -9.9075e-01, -9.5806e-01],
          [-7.4687e-01, -7.5518e-01, -7.2870e-01,  ..., -9.4173e-01,
           -9.6991e-01, -9.5030e-01],
          [-7.9981e-01, -7.9358e-01, -7.9630e-01,  ..., -7.3474e-01,
           -7.4333e-01, -7.3628e-01]]],


        [[[ 6.8056e-01,  6.8056e-01,  6.9105e-01,  ..., -3.6921e-01,
           -3.1641e-01, -3.3400e-01],
          [ 6.9991e-01,  7.1771e-01,  6.8056e-01,  ..., -3.3319e-01,
           -3.4023e-01, -3.8674e-01],
          [ 6.9781e-01,  7.1034e-01,  6.9885e-01,  ..., -2.9567e-01,
           -3.0638e-01, -2.8775e-01],
          ...,
          [-1.4393e+00, -1.4183e+00, -1.4183e+00,  ..., -1.3420e+00,
           -1.4022e+00, -1.3872e+00],
          [-1.4436e+00, -1.4326e+00, -1.4335e+00,  ..., -1.3950e+00,
           -1.3800e+00, -1.3734e+00],
          [-1.4509e+00, -1.4539e+00, -1.4533e+00,  ..., -1.3681e+00,
           -1.4340e+00, -1.3650e+00]],

         [[ 2.0471e+00,  2.0471e+00,  2.0603e+00,  ..., -6.5347e-02,
            2.6326e-02,  3.4833e-02],
          [ 2.0525e+00,  2.0750e+00,  2.0818e+00,  ..., -4.7675e-02,
           -5.2935e-03, -2.6855e-02],
          [ 2.0976e+00,  2.1136e+00,  2.1051e+00,  ...,  1.8606e-02,
            4.1052e-02,  8.5274e-02],
          ...,
          [-1.2304e+00, -1.2244e+00, -1.2219e+00,  ..., -1.2425e+00,
           -1.3041e+00, -1.2836e+00],
          [-1.2239e+00, -1.2107e+00, -1.2107e+00,  ..., -1.2967e+00,
           -1.2813e+00, -1.2746e+00],
          [-1.2210e+00, -1.2154e+00, -1.2157e+00,  ..., -1.2695e+00,
           -1.3401e+00, -1.2696e+00]],

         [[ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  3.4950e-01,
            4.4111e-01,  4.1667e-01],
          [ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  3.3850e-01,
            3.8055e-01,  3.7792e-01],
          [ 2.6400e+00,  2.6400e+00,  2.6400e+00,  ...,  4.4053e-01,
            4.5217e-01,  4.8598e-01],
          ...,
          [-8.2900e-01, -8.1651e-01, -8.1498e-01,  ..., -9.5577e-01,
           -1.0173e+00, -9.9684e-01],
          [-8.3432e-01, -8.2192e-01, -8.2227e-01,  ..., -1.0234e+00,
           -1.0080e+00, -1.0014e+00],
          [-8.3237e-01, -8.2912e-01, -8.2936e-01,  ..., -1.0039e+00,
           -1.0649e+00, -9.9452e-01]]],


        [[[ 2.0699e+00,  1.9477e+00,  2.0700e+00,  ..., -1.5310e+00,
           -1.6490e+00, -1.6860e+00],
          [ 1.8292e+00,  2.1599e+00,  1.8882e+00,  ..., -1.6536e+00,
           -1.6374e+00, -1.6022e+00],
          [ 2.0288e+00,  1.7863e+00,  2.0564e+00,  ..., -1.6149e+00,
           -1.6315e+00, -1.5586e+00],
          ...,
          [-1.4481e+00, -1.3921e+00, -1.4195e+00,  ..., -1.5045e+00,
           -1.5133e+00, -1.5381e+00],
          [-1.4223e+00, -1.3757e+00, -1.3943e+00,  ..., -1.5238e+00,
           -1.5371e+00, -1.5453e+00],
          [-1.4134e+00, -1.4104e+00, -1.4300e+00,  ..., -1.5163e+00,
           -1.5862e+00, -1.5565e+00]],

         [[ 1.5571e+00,  1.4284e+00,  1.8346e+00,  ..., -1.4521e+00,
           -1.6496e+00, -1.6908e+00],
          [ 1.2790e+00,  1.6710e+00,  1.3942e+00,  ..., -1.5838e+00,
           -1.6467e+00, -1.6069e+00],
          [ 1.4661e+00,  1.2568e+00,  1.7123e+00,  ..., -1.5898e+00,
           -1.6761e+00, -1.6212e+00],
          ...,
          [-1.2567e+00, -1.2393e+00, -1.2457e+00,  ..., -1.4077e+00,
           -1.4073e+00, -1.4286e+00],
          [-1.2191e+00, -1.2129e+00, -1.2214e+00,  ..., -1.4193e+00,
           -1.4265e+00, -1.4403e+00],
          [-1.2213e+00, -1.2350e+00, -1.2495e+00,  ..., -1.4075e+00,
           -1.4811e+00, -1.4504e+00]],

         [[ 1.1398e+00,  1.0327e+00,  1.4135e+00,  ..., -1.2147e+00,
           -1.4180e+00, -1.4598e+00],
          [ 8.6931e-01,  1.2768e+00,  1.0129e+00,  ..., -1.3449e+00,
           -1.3906e+00, -1.3518e+00],
          [ 1.1199e+00,  9.0534e-01,  1.2758e+00,  ..., -1.3922e+00,
           -1.4662e+00, -1.4051e+00],
          ...,
          [-8.5999e-01, -8.2594e-01, -8.6729e-01,  ..., -1.0699e+00,
           -1.0976e+00, -1.1388e+00],
          [-8.4630e-01, -8.2145e-01, -8.4266e-01,  ..., -1.1058e+00,
           -1.1325e+00, -1.1478e+00],
          [-8.5198e-01, -8.5977e-01, -8.7435e-01,  ..., -1.1186e+00,
           -1.1739e+00, -1.1579e+00]]]], device='cuda:0')

[[2]]
TensorMask([[[ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         ...,
         [19, 19, 19,  ..., 17, 17, 17],
         [19, 19, 19,  ..., 17, 17, 17],
         [19, 19, 19,  ..., 17, 17, 17]],

        [[ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         [ 4,  4,  4,  ...,  4,  4,  4],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]],

        [[26, 21, 26,  ..., 26, 26, 26],
         [26, 21, 26,  ..., 26, 26, 26],
         [26, 21, 21,  ..., 26, 26, 26],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]],

        ...,

        [[ 4,  4,  4,  ..., 26, 26, 26],
         [ 4,  4,  4,  ..., 26, 26, 26],
         [ 4,  4,  4,  ..., 26, 26, 26],
         ...,
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19]],

        [[21, 21, 21,  ...,  4,  4,  4],
         [21, 21, 21,  ...,  4,  4,  4],
         [21, 21, 21,  ...,  4,  4,  4],
         ...,
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19],
         [17, 17, 17,  ..., 19, 19, 19]],

        [[ 4,  4,  4,  ..., 30, 30, 30],
         [ 4,  4,  4,  ..., 30, 30, 30],
         [ 4,  4,  4,  ..., 30, 30, 30],
         ...,
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17],
         [17, 17, 17,  ..., 17, 17, 17]]], device='cuda:0')

The shape of the tensors:

batch[[1]]$shape;batch[[2]]$shape

torch.Size([8, 3, 200, 266])
torch.Size([8, 200, 266])

Define input and target:

input = batch[[1]]
target = batch[[2]]

Filter Void class:

mask = target != void_code

31 will be filtered as False:

TensorMask([[[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        ...,

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]],

        [[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]]], device='cuda:0')

> (input$argmax(dim=1L)[mask] == target[mask])
tensor([False, False, False,  ..., False, False, False], device='cuda:0')

> (input$argmax(dim=1L)[mask] == target[mask]) %>%
              float()
tensor([0., 0., 0.,  ..., 0., 0., 0.], device='cuda:0')

> (input$argmax(dim=1L)[mask]==target[mask]) %>%
              float() %>% mean()
tensor(0.0011, device='cuda:0')

Resnet34 model architecture for unet:

learn = unet_learner(dls, resnet34(), metrics = acc_camvid)

And finally, fit:

lr = 3e-3
wd = 1e-2

learn %>% fit_one_cycle(2, slice(lr), pct_start = 0.9, wd = wd)

epoch     train_loss  valid_loss  acc_camvid  time
0         1.367869    1.239496    0.666145    00:25
1         0.929434    0.661407    0.839969    00:23

learn %>% show_results(max_n = 1, figsize = c(20,10), vmin = 1, vmax = 30)

Collab (Collaborative filtering)

Call libraries:

library(zeallot)
library(magrittr)

Get data:

URLs_MOVIE_LENS_ML_100k()

Specify column names:

c(user,item,title)  %<-% list('userId','movieId','title')

Read datasets:

ratings = fread('ml-100k/u.data', col.names = c(user,item,'rating','timestamp'))
movies = fread('ml-100k/u.item', col.names = c(item, 'title', 'date', 'N', 'url',
                                                           paste('g',1:19,sep = '')))

Left join on item:

rating_movie = ratings[movies[, .SD, .SDcols=c(item,title)], on = item]

Load data from dataframe (R):

dls = CollabDataLoaders_from_df(rating_movie, seed=42, valid_pct=0.1, bs=64, item_name=title, path='ml-100k')

Build model:

learn = collab_learner(dls, n_factors = 40, y_range=c(0, 5.5))

Start learning:

learn %>% fit_one_cycle(1, 5e-3,  wd = 1e-1)

Get top 1,000 movies:

top_movies = head(unique(rating_movie[ , count := .N, by = .(title)]
                    [order(count,decreasing = T)]
                    [, c('title','count')]),
                   1e3)[['title']]

Find mean ratings for the films:

mean_ratings = unique(rating_movie[ , .(mean = mean(rating)), by = title])

                                          title     mean
   1:                          Toy Story (1995) 3.878319
   2:                          GoldenEye (1995) 3.206107
   3:                         Four Rooms (1995) 3.033333
   4:                         Get Shorty (1995) 3.550239
   5:                            Copycat (1995) 3.302326
  ---
1660:                      Sweet Nothing (1995) 3.000000
1661:                         Mat' i syn (1997) 1.000000
1662:                          B. Monkey (1998) 3.000000
1663:                       You So Crazy (1994) 3.000000
1664: Scream of Stone (Schrei aus Stein) (1991) 3.000000

Extract bias:

movie_bias = learn %>% get_bias(top_movies, is_item = TRUE)

result = data.table(bias = movie_bias,
           title = top_movies)

res = merge(result, mean_ratings, all.y = FALSE)

res[order(bias, decreasing = TRUE)]

                                           title        bias     mean
   1:                           Star Wars (1977)  0.29479960 4.358491
   2:                               Fargo (1996)  0.25264889 4.155512
   3:                      Godfather, The (1972)  0.23247446 4.283293
   4:           Silence of the Lambs, The (1991)  0.22765337 4.289744
   5:                             Titanic (1997)  0.22353025 4.245714
  ---
 996: Children of the Corn: The Gathering (1996) -0.05671900 1.315789
 997:                       Jungle2Jungle (1997) -0.05957306 2.439394
 998:                  Leave It to Beaver (1997) -0.06268980 1.840909
 999:             Speed 2: Cruise Control (1997) -0.06567496 2.131579
1000:           Island of Dr. Moreau, The (1996) -0.07530680 2.157895

Get weights:

movie_w = learn %>% get_weights(top_movies, is_item = TRUE, convert = TRUE)

Visualize with highcharter:

rownames(movie_w) = res$title

highcharter::hchart(princomp(movie_w, cor = TRUE)) %>% highcharter::hc_legend(enabled = FALSE)

Text data

Grab data:

URLs_IMDB()

Specify path and small batch_size because it consumes a lot of GPU:

path = 'imdb'
bs = 20

Create datablock and iterator:

imdb_lm = DataBlock(blocks=list(TextBlock_from_folder(path, is_lm = TRUE)),
                    get_items = partial(get_text_files(),
                    folders = c('train', 'test', 'unsup')),
                    splitter = RandomSplitter(0.1))

dbunch_lm = imdb_lm %>% dataloaders(source = path, path = path, bs = bs, seq_len = 80)

Load a pretrained model and fit:

learn = language_model_learner(dbunch_lm, AWD_LSTM(), drop_mult = 0.3,
                               metrics = list(accuracy, Perplexity()))

learn %>% fit_one_cycle(1, 2e-2, moms = c(0.8, 0.7, 0.8))

Note: AWD_LSTM() can throw an error. In this case find and clean ".fastai" folder.

Medical data

Import dicom data:

img = dcmread('hemorrhage.dcm')

Visualize data with different windowing effects:

dicom_windows = dicom_windows()
scale = list(FALSE, TRUE, dicom_windows$brain, dicom_windows$subdural)
titles = c('raw','normalized','brain windowed','subdural windowed')

library(zeallot)
c(fig, axs[[2]]) %<-% subplots()

for (i in 1:4) {
  img %>% show(scale = scale[[i]],
               ax = axs[[i]],
               title=titles[i])
}

img %>% plot(dpi = 250)

Apply different cmaps:

img %>% show(cmap = cm()$gist_ncar, figsize = c(6,6))
img %>% plot()

Or get dcm matrix and plot with ggplot:

types = c('raw', 'normalized', 'brain', 'subdural')
p_ = list()
for ( i in 1:length(types)) {
  p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i]))
  p_[[i]] = p
}

ggpubr::ggarrange(p_[[1]], p_[[2]], p_[[3]], p_[[4]], labels = types)

Let's try a relatively complex example:

library(ggplot2)

# crop parameters
img = dcmread('hemorrhage.dcm')
res = img %>% mask_from_blur(win_brain()) %>%
  mask2bbox()

types = c('raw', 'normalized', 'brain', 'subdural')

# colors for matrix filling
colors = list(viridis::inferno(30), viridis::magma(30),
              viridis::plasma(30), viridis::cividis(30))
scan_ = c('uniform_blur2d', 'gauss_blur2d')
p_ = list()

for ( i in 1:length(types)) {
  if(i == 3) {
    scan = scan_[1]
  } else if (i==4) {
    scan = scan_[2]
  } else {
    scan = ''
  }

  # crop with x/y_lim functions from ggplot
  if(i==2) {
    p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
                                                         scan = scan),
                                                         colours = colors[[i]])
    p = p + ylim(c(res[[1]][[1]],res[[2]][[1]])) + xlim(c(res[[1]][[2]],res[[2]][[2]]))

  # zoom image (25 %)
  } else if (i==4) {

    img2 = img
    img2 %>% zoom(0.25)
    p = nandb::matrix_raster_plot(img2 %>% get_dcm_matrix(type = types[i],
                                                          scan = scan),
                                                          colours = colors[[i]])
  } else {
    p = nandb::matrix_raster_plot(img %>% get_dcm_matrix(type = types[i],
                                                         scan = scan),
                                                         colours = colors[[i]])
  }

  p_[[i]] = p
}

ggpubr::ggarrange(p_[[1]],
                  p_[[2]],
                  p_[[3]],
                  p_[[4]],
                  labels = paste(types[1:4],
                                 paste(c('','',scan_))[1:4])
                  )

Additional features

Find optimal learning rate

Get optimal learning rate and then fit:

data = model %>% lr_find()
data

# SuggestedLRs(lr_min=0.017378008365631102, lr_steep=0.0020892962347716093)

         lr_rates   losses
1 0.0000001000000 5.349157
2 0.0000001202264 5.231493
3 0.0000001445440 5.087494
4 0.0000001737801 5.068282
5 0.0000002089296 5.043181
6 0.0000002511886 5.023340

Visualize:

highcharter::hchart(data, "line", highcharter::hcaes(y = losses, x = lr_rates ))

Visualize batch

Visualize tensor(s):

# get batch
batch = dls %>% one_batch(convert = TRUE)

# visualize img 9 with transformations
magick::image_read(batch[[1]][[9]])

Mask

Visualize mask:

library(magrittr)
library(fastai)

# original image
fns = get_image_files('camvid/images')
cam_fn = capture.output(fns[0])

# mask
mask_fn = 'camvid/labels/0016E5_01110_P.png'
cam_img = Image_create(cam_fn)

# create mask
tmask = Transform(Mask_create())
mask = tmask(mask_fn)

# visualize
mask %>% to_matrix() %>%
  nandb::matrix_raster_plot(colours = viridis::plasma(3)) + theme(legend.position = "none")

TensorPoints

Load Tiny Mnist:

# download
URLs_MNIST_TINY()

# black and white img
timg = Transform(ImageBW_create)
mnist_fn = "mnist_tiny/valid/3/9007.png"
mnist_img = timg(mnist_fn)

# resize img
pnt_img = TensorImage(mnist_img %>% Image_resize(size = list(28,35)))

# visualize
library(ggplot2)
pnt_img %>% to_matrix() %>% nandb::matrix_raster_plot(colours = c('white','black')) +
  geom_point(aes(x=0, y=0),size=2, colour="red")+
  geom_point(aes(x=0, y=35),size=2, colour="red")+
  geom_point(aes(x=28, y=0),size=2, colour="red")+
  geom_point(aes(x=28, y=35),size=2, colour="red")+
  geom_point(aes(x=9, y=17),size=2, colour="red")+
  theme(legend.position = "none")

Annotations on Tiny COCO

library(magrittr)
library(zeallot)
library(fastai)

URLs_COCO_TINY()

c(images, lbl_bbox) %<-% get_annotations('coco_tiny/train.json')
timg = Transform(ImageBW_create)
idx = 49
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
                       lbl_bbox[[idx]])
coco_img = timg(coco_fn)

tbbox = LabeledBBox(TensorBBox(bbox[[1]]), bbox[[2]])

(#2) [TensorBBox([[ 91.3000,  77.9400, 102.4300,  82.4700],
        [ 27.5800,  77.6500,  40.7600,  82.3400]]),['tv', 'tv']]

Visualize:

library(imager)
coco = imager::load.image(coco_fn)
plot(coco,axes=F)

for ( i in 1:length(bbox[[1]])) {
  rect(bbox[[1]][[i]][[1]],bbox[[1]][[i]][[2]],
       bbox[[1]][[i]][[3]],bbox[[1]][[i]][[4]],
       border = "white", lwd = 2)

  text(bbox[[1]][[i]][[3]]-2.5,bbox[[1]][[i]][[4]]+2.5, labels = bbox[[2]][i],
       offset = 2,
       pos = 2,
       cex = 1,
       col = "white"
  )
}

Alternatively, we could see batch via dataloader:

idx = 3
c(coco_fn,bbox) %<-% list(paste('coco_tiny/train',images[[idx]],sep = '/'),
                          lbl_bbox[[idx]])

coco_bb = function(x) {
 TensorBBox_create(bbox[[1]])
}

coco_lbl = function(x) {
  bbox[[2]]
}

coco_dsrc = Datasets(c(rep(coco_fn,10)),
                     list(Image_create(), list(coco_bb),
                     list( coco_lbl, MultiCategorize(add_na = TRUE) )
                          ), n_inp = 1)

coco_tdl = TfmdDL(coco_dsrc, bs = 9,
                  after_item = list(BBoxLabeler(), PointScaler(),
                                 ToTensor()),
                  after_batch = list(IntToFloatTensor())
                  )

coco_tdl %>% show_batch(dpi = 200)

NN module

To build a custom sequential model and pass it to learner:

nn$Sequential() +
  nn$Conv2d(1L,20L,5L) +
  nn$Conv2d(1L,20L,5L) +
  nn$Conv2d(1L,20L,5L)

Sequential(
  (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)

To specify the name of the layers, one has to pass layer within lists, because torch layers have no name argument:

nn$Sequential() +
  nn$Conv2d(1L,20L,5L) +
  list('my_conv2',nn$Conv2d(1L,20L,5L)) +
  nn$Conv2d(1L,20L,5L) +
  list('my_conv4',nn$Conv2d(1L,20L,5L))

Sequential(
  (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (my_conv2): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
  (my_conv4): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
)

Code of Conduct

Please note that the fastai project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

fastai's People

Contributors

Stargazers

Watchers

Forkers

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fastai's Issues

Can't download using URLs_ADULT_SAMPLE

Hi,
I receive the following error when I try to download the data using URLs_ADULT_SAMPLE().

Error: $ operator is invalid for atomic vectors

Can somebody please assist

fastai loading error

Hi there,

Thanks for great work to bring fastai to R.

In step two, after installing fastai from git hub, I am getting the following error when I try to load the library.

devtools::install_github('henry090/fastai')
library(fastai)

Error: package or namespace load failed for ‘fastai’ in get(method, envir = envir):
lazy-load database 'C:/R-libraries/fastai/R/fastai.rdb' is corrupt
In addition: Warning message:
In get(method, envir = envir) : internal error -3 in R_decompress1

Any help to get around this would be much appreciated.

Many thanks,

Mubashir

Suppression of bs_finder.cpython-36.pyc. 🐞

@henry090 Last commit introduced the suppression of inst/python/fastaibuilt/pycache/bs_finder.cpython-36.pyc.

I was getting a merging error because of this file, I though I merged the new one of the henry090 / fastai master branch here in my fork.

I do not know how this file is generated in the first place. Maybe you should check after it.

Edit : looking at my history I did git rm on this file to try to solve the merging error. That was quite stupid, sorry for that. I just that just re indexing the file on your local folder will do the trix.

Add automl

We could add auto ml for tabular data like in h2o.

Core callbacks

Core callbacks are not implemented from the R side:

https://docs.fast.ai/callback.core.html

Colab TPU example

We need to add a TPU example on Colab. Then add a ref link to the readme file

Error when using model$export()

When training a model like in this notebook or here (md doc to got the session infos).

I got the following error from python :


Quitting from lines 106-107 (Bug_method_export.Rmd) 
Error in py_call_impl(callable, dots$args, dots$keywords) : 
  AttributeError: Can't pickle local object 'make_python_function.<locals>.python_function'

Detailed traceback: 
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastai/learner.py", line 542, in export
    torch.save(self, self.path/fname, pickle_protocol=pickle_protocol)
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/torch/serialization.py", line 372, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/home/erolland/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/torch/serialization.py", line 476, in _save
    pickler.dump(obj)
Calls: <Anonymous> ... withVisible -> eval -> eval -> <Anonymous> -> py_call_impl
Exécution arrêtée

When I am running :

learnR$export(fname = export.pkl)

The error message came from the first notebook. The second md document provide the session infos().

Load a model saved with SaveModelCallback?

Thank you very much for an awesome and much needed package. I'm saving a model after each epoch using SaveModelCallback

nb_epoch <- 8
learn %>% fit_one_cycle(nb_epoch, cbs = SaveModelCallback(every_epoch = TRUE, fname = 'model'))

Say I'm using a new session, and that I'd like to start from the latest model. Is there a way to load this last model with extension .pth that I stored previously? I guess my question is related to issue #115 I've just seen.
Thanks in advance.

To_fp16()

There is one callback describe here : https://docs.fast.ai/callback.fp16.html
Details here : https://docs.fast.ai/callback.fp16.html#Learner.to_fp16

It allow to use less GPU when training a CNN.
The book describe the use of the method when creating the learner, but it can also be used on the learner directly.
Running it in R however create a bug, and I don't see how to use it.

Precisely :

learnR <- dataloader %>% cnn_learner(xresnet50(), metrics = accuracy,  model_dir="fastai_model/") #prettier
learnR$to_fp16()

Then running :
learnR %>% lr_find()

Error in py_call_impl(callable, dots$args, dots$keywords) : AttributeError: 'str' object has no attribute 'type'`

Because now the learner is a string.

GPU device for Data_Loaders

If cuda is available place it to cuda.
Example:

Data_Loaders(train_dl, valid_dl)

https://henry090.github.io/fastai/articles/low.html

NLP blurr

Allow NLP module installation only for Linux

Add NLP examples

A lot of examples need to be added for NLP:

From Kaggle to local, multilabel classification
Question answering
etc.

Tensor

How to slice tensors from R?

Potential error in TextBlock_from_df ?

Hi. I am not sure why but I got trouble running TextBlock_from_df().
I have a data.table looking like the following :

Id | text_article | label

d0fa7568-7d8e-4db9-870f-f9c6f668c17b | What is this study about? This study used data from the National Education Longitudinal Study (NELS:88) to examine the effects of dual enrollment programs for high school students on college degree attainment | national education longitudinal study

I am trying to run :

data_block = DataBlock(
             blocks = list(TextBlock_from_df(text_cols="text_article")),  #List() is required on the R side.
             get_x=ColReader("text_article"), 
             get_y=ColReader('label'))

According to the example here https://docs.fast.ai/text.data.html#TextBlock.from_df and here : https://github.com/EagerAI/fastai#text-data and the fastai books.

But I also tried directly :

data_block = DataBlock(blocks = list(TextBlock_from_df(text_cols="text_article"))) #from fastai doc

Both gave me the following error :

Error: $ operator is invalid for atomic vectors
Traceback:

DataBlock(blocks = list(TextBlock_from_df(text_cols = "text_article")))

TextBlock_from_df(text_cols = "text_article")

do.call(text()$TextBlock$from_df, args)

I put in bold what attract my suspicion. I am not really at home here, I am doing NLP with fastai for the first time, both in R and fastai as well.
I am putting the name of the column directly as a string, "text_article" as I am supposed to do. Based on the examples the dataframe with the text is supposed to be provided after. For what I understand the fastai wrapper is not happy about the text()$TextBlock$from_df).

bs_find

bs_find for tabular data does not work with fast.ai==2.2.7:

Message: KeyError: "None of ['n_iter'] are in the columns"

Detailed traceback:
  File "D:\a\fastai\fastai\check\fastai.Rcheck\fastai\python\fastaibuilt\bs_finder.py", line 161, in bs_find
    with self.no_logging(): self.fit(n_epoch, lr, cbs=cb)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastai\learner.py", line 211, in fit
    self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastai\learner.py", line 162, in _with_events
    self(f'after_{event_type}');  final()
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastai\learner.py", line 141, in __call__
    def __call__(self, event_name): L(event_name).map(self._call_one)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastcore\foundation.py", line 154, in map
    def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastcore\basics.py", line 666, in map_ex
    return list(res)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastcore\basics.py", line 651, in __call__
    return self.func(*fargs, **kwargs)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastai\learner.py", line 145, in _call_one
    for cb in self.cbs.sorted('order'): cb(event_name)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\fastai\callback\core.py", line 44, in __call__
    if self.run and _run: res = getattr(self, event_name, noop)()
  File "D:\a\fastai\fastai\check\fastai.Rcheck\fastai\python\fastaibuilt\bs_finder.py", line 126, in after_fit
    df.set_index('n_iter', inplace=True)
  File "C:\Users\RUNNER~1\AppData\Local\R-MINI~1\envs\R-RETI~1\lib\site-packages\pandas\core\frame.py", line 4727, in set_index
    raise KeyError(f"None of {missing} are in the columns")

NULL args in lists

If the argument in the list is null remove it. For example

args = list(length=NULL)

if(is.null(args$length)) {
args$length <- NULL
}

AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config'

I am using this tutorial for super resolution Unet model. https://cran.r-project.org/web/packages/fastai/vignettes/super_res_gan.html. However, the create_gen_learner function resulted in Error in py_get_attr_impl(x, name, silent) : AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config' error. The basic Unet without unet_config argument works fine. https://github.com/henry090/fastai#unet-example

create_gen_learner = function() {
  unet_learner(dls_gen, arch, loss_func = loss_gen,
               config = unet_config(blur=TRUE, norm_type = "Weight", 
               self_attention = TRUE, y_range = y_range))
}
learn_gen = create_gen_learner()
# Error in py_get_attr_impl(x, name, silent) :
#     AttributeError: module 'fastai.vision.gan' has no attribute 'unet_config'

load_learner does not seems to exist

load_learner() is the function that allow to load a model that has been exported. It does not seems to exist in the wrapper :

Source : https://docs.fast.ai/learner.html#load_learner

Add Kaggle API

Error in preprocessing (proc) in Rstudio

Hi,
I tried to run the tabular learner from rstudio. Running procs = list(FillMissing(),Categorify(),Normalize()), I get the following error: Error: $ operator is invalid for atomic vectors. Is there a fix for that? Thanks.

Add automl feature

We could add auto ml for tabular data like in h2o.automl.

num workers for Dataloaders

If !os=="linux", num_workers always == 0. Fix for all data loaders, e.g. image laoders

Potenial ploting issue inside Rstudio

Hi again.

Running the following :

path_img = 'cassava-leaf-disease-classification/train_images/'

#library(data.table)

labels<-read_csv('cassava-leaf-disease-classification//train.csv')
head(labels)

dataloader <- fastai::ImageDataLoaders_from_df(df=labels, path=path_img, bs=8, seed=6,
                                               num_workers=0, 
                                               item_tfms = Resize(448),
                                               batch_tfms = aug_transforms(size=224, min_scale=0.75))

dataloader %>% show_batch()

learnR <- dataloader %>% cnn_learner(xresnet50(), metrics = accuracy,  model_dir="fastai_model/") #prettier

learnR$to_fp16()

learnR$freeze()

learnR %>% lr_find()

learnR %>% plot_lr_find(dpi = 200)

I can knit and got a md document with the graphs. However, running the cells of the rmarkdown document will not output anything below the line Or inside the plots panels. (while it will happen with keras for example).

I do not know for sure if this is a local config problem, or a change introduced by the commit 8594699.

fix github actions

Fix Github Actions for the dev version of fast.ai (Github version)

Object Detection module

We need to add the object detection Icevision https://github.com/airctic/icevision for fast.ai from R.

Export in the wrong place ?

I am not 100 % sure it is a bug.

Running code from here : https://github.com/Cdk29/Cassava/blob/main/Xresnet.md

Particularly :

I create the learner using :

learnR <- dataloader %>% cnn_learner(xresnet50(), metrics = accuracy(), model_dir="Xresnet_files/") #prettier

But fastai export it elsewhere :

erolland@erolland-GL75-9SE:~/Bureau$ locate xresnet_50.pkl

/home/erolland/Bureau/Cassava/cassava-leaf-disease-classification/train_images/xresnet_50.pkl

I am not sure but I don't think it is the normal behavior of the export of fastai to use the directory of the training set and not the model_dir to export its model.

Colab

Is it possible somehow to work on Colab from RStudio? (GPU)

fastaudio

fastaudio causes an issue while installing in Windows.

Hugging face

Hugging face for fastai. In addition, please add an example!
Works for Mac and Linux!

Fastai>=2.1

Breaking changes since fastai >= 2.1 which requires torch >= 1.7. Needs to be fixed.

check summary of vision models

Cannot print the summary of vision models. Throws error.

Pretty installation

Can we make the installation of fast.ai prettier? e.g:

PyTorch installed successfully (1.7.1)
Fast.ai installed successfully (2.2.7)

In case of error, try-catch and return them for debugging.
At the end print the python path, so the user can figure out which python was used for installation.

CycleGAN example

Download zebra/horse video from youtube. With magick extract frames. And, finally, train the model.

customize a loss function (e.g., vgg16)

dice loss & feature loss, but a general way to customize the loss function will be better. https://github.com/fastai/fastai/blob/master/dev_nbs/course/lesson7-superres.ipynb https://kaggle.com/bigironsphere/loss-function-library-keras-pytorch

Error in py_call_impl

Hello, I followed this tutorial: https://henry090.github.io/fastai/articles/multilabel.html

And hit an error when I ran this line: learn %>% summary()

Error in py_call_impl(callable, dots$args, dots$keywords) :
ValueError: not enough values to unpack (expected 5, got 4)

Detailed traceback:
File "/mnt/hdd/Z_NLP_Classifier/07_Test_03/02_Code/renv/python/r-reticulate/lib/python3.6/site-packages/blurr/modeling/core.py", line 120, in blurr_summary
res = blurr_module_summary(self, *xb)
File "/mnt/hdd/Z_NLP_Classifier/07_Test_03/02_Code/renv/python/r-reticulate/lib/python3.6/site-packages/blurr/modeling/core.py", line 98, in blurr_module_summary
for typ,np,trn,sz,chnged in infos:

Lastly, not sure if this is relevant, I modified the creation of blocks to be the following (whilst tutorial only include hf_arch and hf_tokenizer):
blocks = list(
HF_TextBlock(hf_arch, hf_config, hf_tokenizer, hf_model),
MultiCategoryBlock(encoded=TRUE, vocab=lbl_cols))

Thank you very much in advance!

Check/test

We need tests for:

Shapinterp
Timm model tests/load
GAN test
CycleGAN
Collaborative filtering
installation with install_fastai

Basic tests are enough for now.

class with Module module

Python wrapper functions definition issue. rstudio/reticulate#869

fastai2 = reticulate::import('fastai')
Mnist_Logistic <- reticulate::PyClass(
  classname = 'Mnist_Logistic',
  inherit = fastai2$basics$Module,
  list(
    `__init__` = function(self) {
      self$lin = fastai2$basics$nn$Linear(784L, 10L, bias = TRUE)
      NULL
    },

    forward = function(self, xb) {
      self$lin(xb)
    }
  )
)

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  KeyError: 'self'

Detailed traceback: 
  File "/Users/turgutabdullayev/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastcore/meta.py", line 28, in __new__
    if res.__init__ is not object.__init__: res.__signature__ = _rm_self(inspect.signature(res.__init__))
  File "/Users/turgutabdullayev/Library/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastcore/meta.py", line 20, in _rm_self
    sigd.pop('self')

Error related to magma during training

@henry090 : I am trying to train a xse_resnet50.

During training I got the following error :

R: /opt/conda/conda-bld/magma-cuda101_1583546950098/work/interface_cuda/interface.cpp:901: void magma_queue_create_from_cuda_internal(magma_device_t, cudaStream_t, cublasHandle_t, cusparseHandle_t, magma_queue**, const char*, const char*, int): Assertion `queue->dCarray__ != __null' failed.

It is not a simple out of memory error, it seems to be some kind of memory leak related to magma, similar to related here.

But : I did not find any mention of this bug occurring with fastai, which I would have expect if this thing occurred recurrently, except for this message on this thread : https://forums.fast.ai/t/a-walk-with-fastai2-vision-study-group-and-online-lectures-megathread/59929/1293 :

The only “new” thing I am doing is that I am encapsulating most of my code for training the model in a try/except block in a while loop.

I wonder if the memory leak is not somehow due to using a function as a wrapper or reticulate.
Link towards the code and error : https://www.kaggle.com/cdk292/magma-error-xse-resnext50-with-r?scriptVersionId=50229515

The last version is still compiling but you can see in the log of execution of version 4 the error, and will probably shown up again in V6.

PS : merry Christmas.

predict dicom data

dicom test does not work with fast.ai==2.2.7:

Message: RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[1, 1, 224, 224] to have 3 channels, but got 1 channels instead

Detailed traceback:
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 243, in get_preds
    self._do_epoch_validate(dl=dl)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 193, in _do_epoch_validate
    with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 160, in _with_events
    try: self(f'before_{event_type}');  f()
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 166, in all_batches
    for o in enumerate(self.dl): self.one_batch(*o)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 184, in one_batch
    self._with_events(self._do_one_batch, 'batch', CancelBatchException)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 160, in _with_events
    try: self(f'before_{event_type}');  f()
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/learner.py", line 169, in _do_one_batch
    self.pred = self.model(*self.xb)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/container.py", line 117, in forward
    input = module(input)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/container.py", line 117, in forward
    input = module(input)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/module.py", line 727, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 423, in forward
    return self._conv_forward(input, self.weight)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 419, in _conv_forward
    return F.conv2d(input, weight, self.bias, self.stride,
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/fastai/torch_core.py", line 329, in __torch_function__
    res = super().__torch_function__(func, types, args=args, kwargs=kwargs)
  File "/Users/runner/Library/r-miniconda/envs/r-reticulate/lib/python3.9/site-packages/torch/tensor.py", line 995, in __torch_function__
    ret = func(*args, **kwargs)

Missing vision models

@henry090 I just realized that some models are missing from fastai :

https://docs.fast.ai/vision.models.xresnet.html

Namely all the models such as xse_resnext50.

I could add them too if you don't mind.

Examples

We need to have examples for:

tensor slicing
layers parameters modification (in-place)
update audio Rmd file
replace values inside tensors

Number of workers

Data loaders with num_workers arguments have to be set by default to 0L for Windows.

TPU and the fastai wrapper

Hi,

I was thinking on the how to train xse-resnext-50 in R without going out of memory. Huge networks like this seems to be trained in Pytorch using TPU on Kaggle.

Fastai does not handle directly TPU but there is some wrapper : https://github.com/butchland/fastai_xla_extensions
Example here : https://www.kaggle.com/johnyquest/tpu-fastai-notebook

I put this as a feature request and a reminder to myself to look into this. As far as I can see it does not seems to much difficult to use TPU with fastai (four pip install and a methods on the learner).
But I don't have your experience with the wrapper/TPU (As far as I know you are the only person that created a kernel with TPU inside R on kaggle :p )

Is it as "easy" as it seems or is there a problem I do not see ?

CRAN 2.0.2

Fix all the issues and submit fastai 2.1.5.

2.1.8 has a lot of bugs:

medical, read dcm
shap, fastinference
summary of the model

New tutorials

Add Bayesian Optimization tutorial with fast.ai

Error with pycocotools

Please briefly describe your problem and what output you expect.
I got the following error when trying to install the fastai package using r.

I think it wants to convince me to purchase Microsoft Visual C++ 14.0, but that is not in my budget. I'm still paying off my student loans. (@michael Nikkhoo)

My command was:
fastai::install_fastai(gpu = FALSE, cuda_version = '10.1', overwrite = FALSE)

There was a ton of text and then this error in the middle:

copying pycocotools\cocoeval.py -> build\lib.win-amd64-3.6\pycocotools
    copying pycocotools\mask.py -> build\lib.win-amd64-3.6\pycocotools
    copying pycocotools\__init__.py -> build\lib.win-amd64-3.6\pycocotools
    running build_ext
    skipping 'pycocotools\_mask.c' Cython extension (up-to-date)
    building 'pycocotools._mask' extension
    error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio": https://visualstudio.microsoft.com/downloads/
    ----------------------------------------

Then it gave me this as the final result:

ERROR: Command errored out with exit status 1: 'C:\Users\<username>\AppData\Local\R-MINI~1\envs\R-RETI~1\python.exe' -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\<username>\\AppData\\Local\\Temp\\pip-install-hbabtz_i\\pycocotools_88fb2b1f63764ff4b73954ac6b86a7da\\setup.py'"'"'; __file__='"'"'C:\\Users\\<username>\\AppData\\Local\\Temp\\pip-install-hbabtz_i\\pycocotools_88fb2b1f63764ff4b73954ac6b86a7da\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' install --record 'C:\Users\<username>\AppData\Local\Temp\pip-record-64h7kl2e\install-record.txt' --single-version-externally-managed --compile --install-headers 'C:\Users\<username>\AppData\Local\R-MINI~1\envs\R-RETI~1\Include\pycocotools' Check the logs for full command output.
Error: Error installing package(s): "torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio===0.7.0 -f https://download.pytorch.org/whl/torch_stable.html", "IPython", "fastai==2.1.5", "pydicom", "kornia", "opencv-python", "scikit-image", "kaggle", "transformers", "timm", "fastinference[interp]", "ohmeow-blurr", "icevision[all]", "git+https://github.com/tcapelle/timeseries_fastai.git", "git+https://github.com/tmabraham/UPIT.git", "opencv-python-headless==4.4.0.46"

Of course, usernames have been changed to protect the innocent...

Mac OS fastai

fast.ai for Mac OS installs first torch 1.8 then 1.7.1. We need to install pkgs separately, so unnecessary installation will not be required. First, install fastai (try).

Issues with package in Kaggle

Thanks again for doing this package. Since you seem to use Kaggle notebooks yourself, do you know what's up with the installed R package that is now part of the standard R Kaggle set-up? I tried a few things and just could not get it to work. It looks to me like the python fastai package is missing in the background (I also posted this on Kaggle), but my attempt to simply use fastai::install_fastai(gpu = FALSE, cuda_version = '10.1', overwrite = FALSE) failed. Do you know how to get this to work (great if you could share that)? I would of course also understand if you feel that fixing Kaggle's setup is not your problem.

Colab and fast.ai

Can we make the model training interactive in Colab. Image plotting is not good.

Update examples

No more custom installation of fast audio and other minor issues.

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